code: def evaluate(model, data_loader, device):
      n_threads = torch.get_num_threads()
      # FIXME remove this and make paste_masks_in_image run on the GPU
      torch.set_num_threads(1)
      cpu_device = torch.device("cpu")
      model.eval()
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = "Test:"
  
      coco = get_coco_api_from_dataset(data_loader.dataset)
      iou_types = _get_iou_types(model)
      coco_evaluator = CocoEvaluator(coco, iou_types)
  
      for images, targets in metric_logger.log_every(
          data_loader, 100, header
      ):
          images = list(img.to(device) for img in images)
  
          if torch.cuda.is_available():
              torch.cuda.synchronize()
          model_time = time.time()
          outputs = model(images)
  
          outputs = [
              {k: v.to(cpu_device) for k, v in t.items()}
              for t in outputs
          ]
          model_time = time.time() - model_time
  
          res = {
              target["image_id"]: output
              for target, output in zip(targets, outputs)
          }
          evaluator_time = time.time()
          coco_evaluator.update(res)
          evaluator_time = time.time() - evaluator_time
          metric_logger.update(
              model_time=model_time, evaluator_time=evaluator_time
          )
  
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print("Averaged stats:", metric_logger)
      coco_evaluator.synchronize_between_processes()
  
      # accumulate predictions from all images
      coco_evaluator.accumulate()
      coco_evaluator.summarize()
      torch.set_num_threads(n_threads)
      return coco_evaluator
  
file_path: vision/references/detection/engine.py
imports: import math
  import sys
  import time
  import torch
  import torchvision.models.detection.mask_rcnn
  import utils
  from coco_eval import CocoEvaluator
  from coco_utils import get_coco_api_from_dataset
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(model, data_loader, device) and returns: coco_evaluator